Multi-scale Object-Aware Gaze Estimation via Geometric Reasoning

• URL: http://arxiv.org/abs/2606.29334v1
• Date: Sun, 28 Jun 2026 11:02:21 GMT
• Title: Multi-scale Object-Aware Gaze Estimation via Geometric Reasoning
• Abstract summary: We propose a two-stage gaze estimation framework guided by object semantics.<n>Our method achieves strong performance across all benchmarks while maintaining a compact parameter size of 7.1M.
• Score: 22.665947623978
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gaze target estimation aims to predict the semantic object an observer fixates upon within an image, a task deeply rooted in the object-oriented nature of human gaze. Observers tend to select a specific semantic entity as the attentional target, rather than responding randomly across arbitrary regions of the image. However, existing methods typically model this task as a direct mapping from global features to gaze heatmaps, essentially treating it as a pixel-level regression problem. This approach fails to explicitly represent the gazed object as a distinct entity, making it difficult to produce stable and semantically consistent predictions in complex scenes. To address this, we propose a two-stage gaze estimation framework guided by object semantics, reformulating gaze target estimation as a hierarchical reasoning process. Our method incorporates object-level representations during feature encoding to align image features with discrete semantic entities, then introduces multi-scale feature fusion and geometric constraints from head pose and gaze direction for fine-grained localization and object-level discrimination. Extensive experiments on GazeFollow, VideoAttentionTarget, ChildPlay, and GOO-Real demonstrate that our method achieves AUC of 0.961, 0.948, 0.987, and 0.977 respectively, delivering strong performance across all benchmarks while maintaining a compact parameter size of 7.1M.

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